


from ...configuration_utils import PretrainedConfig
from ...utils import logging


a			:			Optional[Any]												=					logging.get_logger(__name__)

a			:			Any												=					{
    '''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					'nllb-moe'
					__UpperCAmelCase							  =					['past_key_values']
					__UpperCAmelCase							  =					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






					def __init__(					self			,							snake_case_=1_2_8_1_1_2			,							snake_case_=1_0_2_4			,							snake_case_=1_2			,							snake_case_=4_0_9_6			,							snake_case_=1_6			,							snake_case_=1_2			,							snake_case_=4_0_9_6			,							snake_case_=1_6			,							snake_case_=0.0_5			,							snake_case_=0.0_5			,							snake_case_=True			,							snake_case_=True			,							snake_case_="relu"			,							snake_case_=1_0_2_4			,							snake_case_=0.1			,							snake_case_=0.1			,							snake_case_=0.0			,							snake_case_=0.0_2			,							snake_case_=2			,							snake_case_=True			,							snake_case_=False			,							snake_case_="float32"			,							snake_case_=False			,							snake_case_=1_2_8			,							snake_case_=6_4			,							snake_case_=4			,							snake_case_=4			,							snake_case_=0.0_0_1			,							snake_case_=0.0_0_1			,							snake_case_="all"			,							snake_case_=False			,							snake_case_=False			,							snake_case_=1.0			,							snake_case_=0.2			,							snake_case_=1			,							snake_case_=0			,							snake_case_=2			,							snake_case_=False			,							**snake_case_			,							) ->      Dict:







								'''simple docstring'''

								__lowercase			       = vocab_size
								__lowercase			       = max_position_embeddings
								__lowercase			       = d_model
								__lowercase			       = encoder_ffn_dim
								__lowercase			       = encoder_layers
								__lowercase			       = encoder_attention_heads
								__lowercase			       = decoder_ffn_dim
								__lowercase			       = decoder_layers
								__lowercase			       = decoder_attention_heads
								__lowercase			       = dropout
								__lowercase			       = attention_dropout
								__lowercase			       = activation_dropout
								__lowercase			       = activation_function
								__lowercase			       = init_std
								__lowercase			       = encoder_layerdrop
								__lowercase			       = decoder_layerdrop
								__lowercase			       = use_cache
								__lowercase			       = encoder_layers
								__lowercase			       = scale_embedding  # scale factor will be sqrt(d_model) if True
								__lowercase			       = router_z_loss_coef
								__lowercase			       = router_aux_loss_coef
								__lowercase			       = decoder_sparse_step
								__lowercase			       = encoder_sparse_step
								__lowercase			       = num_experts
								__lowercase			       = expert_capacity
								__lowercase			       = router_bias
								if router_dtype not in ["float32", "float16", "bfloat16"]:
											raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'    )
								__lowercase			       = router_dtype

								__lowercase			       = router_ignore_padding_tokens
								__lowercase			       = batch_prioritized_routing
								__lowercase			       = second_expert_policy
								__lowercase			       = normalize_router_prob_before_dropping
								__lowercase			       = moe_eval_capacity_token_fraction
								__lowercase			       = moe_token_dropout
								__lowercase			       = output_router_logits
								super().__init__(
								    pad_token_id=snake_case_			,							bos_token_id=snake_case_			,							eos_token_id=snake_case_			,							is_encoder_decoder=snake_case_			,							decoder_start_token_id=snake_case_			,							**snake_case_			,							)






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard







@slow
@require_flax
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-canny"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"bird"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([canny_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-openpose"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"Chef in the kitchen"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([pose_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2


